R Markdown

Introduction

This is gshelley13’s first assignment for Geog458. Our course page can be accessed here.

Background

On the weekends, when I’m not doing homework for Geog458, I go to this ski mountain

On the weekends, when I’m not doing homework for Geog458, I go to this ski mountain

When thinking about mass-energy equivalence I use the equation \[E=mc^2\]

Name Age
Beck 15
Angie 20
Ray 16
DJ 19
Chris 23
Ami 21 
100/10+2
## [1] 12
100/(10+2)
## [1] 8.333333
25+3/4-6/100
## [1] 25.69
(25+3)/(4-6)/100
## [1] -0.14
1+250/40/(15-9)
## [1] 2.041667
x<-8*3
x+10
## [1] 34
(x/2)+(x*3)
## [1] 84
z<-9 + 6
y<-2 * 14
x + y
## [1] 52
(6*z) - (0.5*y)
## [1] 76
qt<-seq(1,30)
qt
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30
c(qt,qt,qt)
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16
## [47] 17 18 19 20 21 22 23 24 25 26 27 28 29 30  1  2  3  4  5  6  7  8  9
## [70] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
length(qt)
## [1] 30
length(c(qt,qt,qt))
## [1] 90
sum(qt)
## [1] 465
sum(c(qt,qt,qt))
## [1] 1395
bond<-seq(1,5)
james<-seq(6,10)
bond + james
## [1]  7  9 11 13 15
bond * james
## [1]  6 14 24 36 50
c(bond,james)
##  [1]  1  2  3  4  5  6  7  8  9 10
jamie<-seq(1,5)
lee<-seq(6,10) 
curtis<-jamie*lee
rbind(jamie,lee,curtis)
##        [,1] [,2] [,3] [,4] [,5]
## jamie     1    2    3    4    5
## lee       6    7    8    9   10
## curtis    6   14   24   36   50
g<-rbind(jamie,lee,curtis)
data.frame(rbind(jamie,lee,curtis))
##        X1 X2 X3 X4 X5
## jamie   1  2  3  4  5
## lee     6  7  8  9 10
## curtis  6 14 24 36 50

R Markdown

This is how to load data into R and how to convert it.

library(tidyverse)
## ── Attaching packages ─────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.0     ✔ purrr   0.2.5
## ✔ tibble  2.0.1     ✔ dplyr   0.7.8
## ✔ tidyr   0.8.2     ✔ stringr 1.3.1
## ✔ readr   1.3.1     ✔ forcats 0.3.0
## ── Conflicts ────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
object1=read.csv("/Users/gabbyshelley/Desktop/GIS directory/China_EO_49to171.csv",fileEncoding = "latin1")
object2=as_tibble(object1)
object2
## # A tibble: 69 x 63
##     Year Beijing_Enterpr… Beijing_Output Tianjin_Enterpr… Tianjin_Output
##    <int>            <int>          <dbl>            <int>          <dbl>
##  1  1949            21055           1.47             4708           7.29
##  2  1950            23461           3.07             6358          11.6 
##  3  1951            29839           5.99             9190          17.3 
##  4  1952            34386           7.19               82          18.5 
##  5  1953            38632          10.3              8459          26.2 
##  6  1954            39595          11.9              8918          29.4 
##  7  1955            32036          13.2              7250          30.1 
##  8  1956             3574          18.2              2710          35.7 
##  9  1957             4234          19.9              1049          38.6 
## 10  1958             2084          42.8              2376          61.9 
## # … with 59 more rows, and 58 more variables: Hebei_Enterprise <int>,
## #   Hebei_Output <dbl>, Shanxi_Enterprise <int>, Shanxi_Output <dbl>,
## #   InnerMongolia_Enterprise <int>, InnerMongolia_Output <dbl>,
## #   Liaoning_Enterprise <int>, Liaoning_Output <dbl>,
## #   Jilin_Enterprise <int>, Jilin_Output <dbl>,
## #   Heilongjiang_Enterprise <int>, Heilongjiang_Output <dbl>,
## #   Shanghai_Enterprise <int>, Shanghai_Output <dbl>,
## #   Jiangsu_Enterprise <int>, Jiangsu_Output <dbl>,
## #   Zhejiang_Enterprise <int>, Zhejiang_Output <dbl>,
## #   Anhui_Enterprise <int>, Anhui_Output <dbl>, Fujian_Enterprise <int>,
## #   Fujian_Output <dbl>, Jiangxi_Enterprise <int>, Jiangxi_Output <dbl>,
## #   Shandong_Enterprise <int>, Shandong_Output <dbl>,
## #   Henan_Enterprise <int>, Henan_Output <dbl>, Hubei_Enterprise <int>,
## #   Hubei_Output <dbl>, Hunan_Enterprises <int>, Hunan_Output <dbl>,
## #   Guangdong_Enterprise <int>, Guangdong_Output <dbl>,
## #   Guangxi_Enterprise <int>, Guangxi_Output <dbl>,
## #   Hainan_Enterprise <int>, Hainan_Output <dbl>,
## #   Chongqing_Enterprise <int>, Chongqing_Output <dbl>,
## #   Sichuan_Enterprise <int>, Sichuan_Output <dbl>,
## #   Guizhou_Enterprise <int>, Guizhou_Output <dbl>,
## #   Yunnan_Enterprise <int>, Yunnan_Output <dbl>, Tibet_Enterprise <int>,
## #   Tibet_Output <dbl>, Shaanxi_Enterprise <int>, Shaanxi_Output <dbl>,
## #   Gansu_Enterprise <int>, Gansu_Output <dbl>, Qinghai_Enterprise <int>,
## #   Qinghai_Output <dbl>, Ningxia_Enterprise <int>, Ningxia_Output <dbl>,
## #   Xinjiang_Enterprise <int>, Xinjiang_Output <dbl>
arrange(object2,desc(Year))
## # A tibble: 69 x 63
##     Year Beijing_Enterpr… Beijing_Output Tianjin_Enterpr… Tianjin_Output
##    <int>            <int>          <dbl>            <int>          <dbl>
##  1  2017             3231            NA              4286            NA 
##  2  2016             3340         18087.             5203         27402.
##  3  2015             3548         17450.             5525         28017.
##  4  2014             3686         18453.             5501         28079.
##  5  2013             3641         17371.             5511         26400.
##  6  2012             3692         15596.             5342         23428.
##  7  2011             3746         14514.             5013         20863.
##  8  2010             6884         13700.             7947         16752.
##  9  2009             6890         11039.             8326         13084.
## 10  2008             7205         10413.             7950         12503.
## # … with 59 more rows, and 58 more variables: Hebei_Enterprise <int>,
## #   Hebei_Output <dbl>, Shanxi_Enterprise <int>, Shanxi_Output <dbl>,
## #   InnerMongolia_Enterprise <int>, InnerMongolia_Output <dbl>,
## #   Liaoning_Enterprise <int>, Liaoning_Output <dbl>,
## #   Jilin_Enterprise <int>, Jilin_Output <dbl>,
## #   Heilongjiang_Enterprise <int>, Heilongjiang_Output <dbl>,
## #   Shanghai_Enterprise <int>, Shanghai_Output <dbl>,
## #   Jiangsu_Enterprise <int>, Jiangsu_Output <dbl>,
## #   Zhejiang_Enterprise <int>, Zhejiang_Output <dbl>,
## #   Anhui_Enterprise <int>, Anhui_Output <dbl>, Fujian_Enterprise <int>,
## #   Fujian_Output <dbl>, Jiangxi_Enterprise <int>, Jiangxi_Output <dbl>,
## #   Shandong_Enterprise <int>, Shandong_Output <dbl>,
## #   Henan_Enterprise <int>, Henan_Output <dbl>, Hubei_Enterprise <int>,
## #   Hubei_Output <dbl>, Hunan_Enterprises <int>, Hunan_Output <dbl>,
## #   Guangdong_Enterprise <int>, Guangdong_Output <dbl>,
## #   Guangxi_Enterprise <int>, Guangxi_Output <dbl>,
## #   Hainan_Enterprise <int>, Hainan_Output <dbl>,
## #   Chongqing_Enterprise <int>, Chongqing_Output <dbl>,
## #   Sichuan_Enterprise <int>, Sichuan_Output <dbl>,
## #   Guizhou_Enterprise <int>, Guizhou_Output <dbl>,
## #   Yunnan_Enterprise <int>, Yunnan_Output <dbl>, Tibet_Enterprise <int>,
## #   Tibet_Output <dbl>, Shaanxi_Enterprise <int>, Shaanxi_Output <dbl>,
## #   Gansu_Enterprise <int>, Gansu_Output <dbl>, Qinghai_Enterprise <int>,
## #   Qinghai_Output <dbl>, Ningxia_Enterprise <int>, Ningxia_Output <dbl>,
## #   Xinjiang_Enterprise <int>, Xinjiang_Output <dbl>
select(object2, Year, Beijing_Enterprise, Beijing_Output, Shanghai_Enterprise, Shanghai_Output)
## # A tibble: 69 x 5
##     Year Beijing_Enterpri… Beijing_Output Shanghai_Enterpr… Shanghai_Output
##    <int>             <int>          <dbl>             <int>           <dbl>
##  1  1949             21055           1.47             20307            NA  
##  2  1950             23461           3.07             20897            NA  
##  3  1951             29839           5.99             24956            NA  
##  4  1952             34386           7.19             25878            66.6
##  5  1953             38632          10.3              29873            91.5
##  6  1954             39595          11.9              28860            96.4
##  7  1955             32036          13.2              23713            91.4
##  8  1956              3574          18.2              18724           114. 
##  9  1957              4234          19.9              16316           119. 
## 10  1958              2084          42.8              14240           176. 
## # … with 59 more rows
comp<-select(object2, Year, Beijing_Enterprise, Beijing_Output, Shanghai_Enterprise, Shanghai_Output)
filter(comp, Year == 2000 | Year == 2001 | Year == 2003 | Year == 2004 | Year == 2005 | Year == 2006 | Year == 2007 | Year == 2008 | Year == 2009 | Year == 2010 | Year == 2011 | Year == 2012 | Year == 2013 | Year == 2014 | Year == 2015 | Year == 2016 | Year == 2017)
## # A tibble: 17 x 5
##     Year Beijing_Enterpri… Beijing_Output Shanghai_Enterpr… Shanghai_Output
##    <int>             <int>          <dbl>             <int>           <dbl>
##  1  2000              4572          2565.              8574           6205.
##  2  2001              4356          2909.              9762           7004.
##  3  2003              4019          3810.             11098          10343.
##  4  2004              6871          4881.             15766          12885.
##  5  2005              6300          6946.             14809          15768.
##  6  2006              6400          8210              14404          18573.
##  7  2007              6397          9648.             15099          22260.
##  8  2008              7205         10413.             18792          25121.
##  9  2009              6890         11039.             17906          24091.
## 10  2010              6884         13700.             16684          30114.
## 11  2011              3746         14514.              9962          32445.
## 12  2012              3692         15596.              9772          31548.
## 13  2013              3641         17371.              9796          32089.
## 14  2014              3686         18453.              9469          32665.
## 15  2015              3548         17450.              8994          31050.
## 16  2016              3340         18087.              8351          31136.
## 17  2017              3231            NA               8122          36094.
task14 <- filter(comp, Year == 2000 | Year == 2001 | Year == 2003 | Year == 2004 | Year == 2005 | Year == 2006 | Year == 2007 | Year == 2008 | Year == 2009 | Year == 2010 | Year == 2011 | Year == 2012 | Year == 2013 | Year == 2014 | Year == 2015 | Year == 2016 | Year == 2017)
task14
## # A tibble: 17 x 5
##     Year Beijing_Enterpri… Beijing_Output Shanghai_Enterpr… Shanghai_Output
##    <int>             <int>          <dbl>             <int>           <dbl>
##  1  2000              4572          2565.              8574           6205.
##  2  2001              4356          2909.              9762           7004.
##  3  2003              4019          3810.             11098          10343.
##  4  2004              6871          4881.             15766          12885.
##  5  2005              6300          6946.             14809          15768.
##  6  2006              6400          8210              14404          18573.
##  7  2007              6397          9648.             15099          22260.
##  8  2008              7205         10413.             18792          25121.
##  9  2009              6890         11039.             17906          24091.
## 10  2010              6884         13700.             16684          30114.
## 11  2011              3746         14514.              9962          32445.
## 12  2012              3692         15596.              9772          31548.
## 13  2013              3641         17371.              9796          32089.
## 14  2014              3686         18453.              9469          32665.
## 15  2015              3548         17450.              8994          31050.
## 16  2016              3340         18087.              8351          31136.
## 17  2017              3231            NA               8122          36094.
mutate(task14,
       Output_Ratio = Beijing_Output/Shanghai_Output)
## # A tibble: 17 x 6
##     Year Beijing_Enterpr… Beijing_Output Shanghai_Enterp… Shanghai_Output
##    <int>            <int>          <dbl>            <int>           <dbl>
##  1  2000             4572          2565.             8574           6205.
##  2  2001             4356          2909.             9762           7004.
##  3  2003             4019          3810.            11098          10343.
##  4  2004             6871          4881.            15766          12885.
##  5  2005             6300          6946.            14809          15768.
##  6  2006             6400          8210             14404          18573.
##  7  2007             6397          9648.            15099          22260.
##  8  2008             7205         10413.            18792          25121.
##  9  2009             6890         11039.            17906          24091.
## 10  2010             6884         13700.            16684          30114.
## 11  2011             3746         14514.             9962          32445.
## 12  2012             3692         15596.             9772          31548.
## 13  2013             3641         17371.             9796          32089.
## 14  2014             3686         18453.             9469          32665.
## 15  2015             3548         17450.             8994          31050.
## 16  2016             3340         18087.             8351          31136.
## 17  2017             3231            NA              8122          36094.
## # … with 1 more variable: Output_Ratio <dbl>
final <- mutate(task14,
       Output_Ratio = Beijing_Output/Shanghai_Output)
final
## # A tibble: 17 x 6
##     Year Beijing_Enterpr… Beijing_Output Shanghai_Enterp… Shanghai_Output
##    <int>            <int>          <dbl>            <int>           <dbl>
##  1  2000             4572          2565.             8574           6205.
##  2  2001             4356          2909.             9762           7004.
##  3  2003             4019          3810.            11098          10343.
##  4  2004             6871          4881.            15766          12885.
##  5  2005             6300          6946.            14809          15768.
##  6  2006             6400          8210             14404          18573.
##  7  2007             6397          9648.            15099          22260.
##  8  2008             7205         10413.            18792          25121.
##  9  2009             6890         11039.            17906          24091.
## 10  2010             6884         13700.            16684          30114.
## 11  2011             3746         14514.             9962          32445.
## 12  2012             3692         15596.             9772          31548.
## 13  2013             3641         17371.             9796          32089.
## 14  2014             3686         18453.             9469          32665.
## 15  2015             3548         17450.             8994          31050.
## 16  2016             3340         18087.             8351          31136.
## 17  2017             3231            NA              8122          36094.
## # … with 1 more variable: Output_Ratio <dbl>

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this: